Quantitative Engineering

Job Summary

Corporate Treasury (CT) lies at the heart of Goldman Sachs, ensuring all the businesses have the appropriate level of funding to conduct their activities, while also optimizing the firm’s liquidity and managing its risk. 

The mission statement of the Resource Allocation (RA) Strats team within CT is to develop quantitative analytics to inform and advance firmwide liability funding and execution, risk limits, and incentives. We partner with senior leadership across the firm to manage and execute the firm’s central liabilities across unsecure, secured and deposit channels; set first line risk appetite and internal funds transfer incentives for assets and liabilities businesses; and construct the firm’s funding plans for BAU, budget and stress.

RA’s primary objectives are:

• Optimize the firm’s liability stack across diverse channels, products, tenors
• Create benchmarks to track execution of central liabilities
• First line risk appetite and limits calibration
• FTP incentives for assets and liabilities businesses

Responsibilities:

• Leadership to build out SLC Strat team for RA over time
• Develop quantitative frameworks and mathematical models to optimize the firm’s liabilities
• Understand business needs, data requirements and specifications; facilitate and develop process workflow required to support implementation of data engineering solutions
• Understand and modeling regulatory constraints impacting the Firm’s balance sheet: liquidity, leverage, capital, market limits
• Develop analytics to inform BAU unsecured funding, execution and limits management
• Analyze model output and facilitate understanding of model results by non-technical clients
• Manage/Execute end-to-end systems development cycle from requirements analysis, coding, testing, UAT and post go live maintenance

Basic Qualifications

• Advanced degrees (PhD or Masters) in quantitative field such as Engineering. Computer Science, Mathematics, Physics -- or bachelor’s with relevant work experience
• 7+ years of relevant experience
• Strong analytical and problem-solving ability
• Python or similar programming language
• Excellent communication skills, including experience speaking to both technical and business audiences and working globally across multiple regions
• Familiarity with financial markets, liquidity/market/capital risk a plus
• Self-motivated team player